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  • sknn.mlp — Multi-Layer Perceptrons
  • Installation
  • Model Setup & Training
  • Low-Level Configuration
  • Input / Output
  • scikit-learn Features
  • Customizing Learning
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Welcome to sknn’s documentation!¶

Deep neural network implementation without the learning cliff! This library implements multi-layer perceptrons as a wrapper for the powerful pylearn2 library that’s compatible with scikit-learn for a more user-friendly and Pythonic interface.

Build Status Code Coverage License Type Source Code


_images/plot_activation.png

Module Reference¶

  • sknn.mlp — Multi-Layer Perceptrons
    • Layer Specifications
    • MultiLayerPerceptron
    • Regressor
    • Classifier

User Guide¶

  • Installation
    • A) Download Latest Release [Recommended]
    • B) Pulling Repositories [Optional]
  • Model Setup & Training
    • Regression
    • Classification
    • Convolution
    • Per-Sample Weighting
    • Native & Custom Layers
  • Low-Level Configuration
    • Keyboard Interrupt
    • CPU vs. GPU Platform
    • Multiple Threads
    • Backend Configuration
  • Input / Output
    • Verbose Mode
    • Saving & Loading
    • Extracting Parameters
  • scikit-learn Features
    • sklearn Pipeline
    • Grid Search
    • Randomized Search
    • Unsupervised Pre-Training
  • Customizing Learning
    • Training Callbacks
    • Epoch Statistics

Indices & Search¶

  • Index
  • Search Page

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© Copyright 2015, scikit-neuralnetwork developers (BSD License). Revision b7fd0c08.

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